Traffic control agency deployment and signal optimization for event planning

ABSTRACT

Embodiments relate to traffic control resource planning. An aspect includes receiving information about available routes in a transportation network and receiving an estimate of a traffic demand in the transportation network. Traffic control planning is performed and it may include: simulating a traffic flow based on the available routes and the traffic demand; applying a model that varies traffic control agent (TCA) placement and traffic signal settings in the transportation network to minimize a cost associated with the traffic flow, the cost including a TCA deployment cost and a traffic delay cost; and outputting a traffic control plan based on the applying, the traffic control plan including a TCA placement and traffic signal setting plan.

BACKGROUND

The present invention relates generally to event planning, and morespecifically to traffic agency deployment and signal optimization forevent planning.

Large scale planned events, such as sporting events and parades, attracthigh volumes of both pedestrians and vehicles (e.g., buses, passengervehicles), often resulting in significant non-recurrent congestion onlocal transportation networks in the vicinity of the events. The localtransportation networks, including the roadways used to travel to theevents, are often overloaded by the additional demand as attendeessimultaneously attempt to enter or exit the event. Traditionally,planning for the management of this congestion has been performedmanually by individuals, such as traffic control managers, who use theirpast experiences to determine how to deploy traffic control agencyresources in an effort to minimize bottlenecks.

Unplanned events, such as traffic incidents, severe weather, andfacility problems, may also cause significant non-recurrent congestionto roadways. Non-recurrent congestion caused by unplanned events isoften due to a restriction in capacity because of damaged or disabledtraffic lanes or other disabled roadway infrastructures. Similar toplanned events, the management of congestion caused by unplanned eventsis performed manually by individuals based on their past experiences.

SUMMARY

Embodiments include a method, system, and computer program product fortraffic control resource planning. The method may include receivinginformation about available routes in a transportation network andreceiving an estimate of a traffic demand in the transportation network.Traffic control planning is performed and it may include: simulating atraffic flow based on the available routes and the traffic demand;applying a model that varies traffic control agent (TCA) placement andtraffic signal settings in the transportation network to minimize a costassociated with the traffic flow, the cost including a TCA deploymentcost and a traffic delay cost; and outputting a traffic control planbased on the applying, the traffic control plan including a TCAplacement and traffic signal setting plan.

Additional features and advantages are realized through the techniquesof embodiments of the present invention. Other embodiments and aspectsof embodiments of the invention are described in detail herein. For abetter understanding of embodiments of the present invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofembodiments of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts a framework for special event planning that may beimplemented by an embodiment;

FIG. 2 depicts a block diagram of a process for traffic control agent(TCA) deployment and signal optimization that may be implemented by anembodiment;

FIG. 3 depicts a flow diagram of a process for daily traffic controlagency planning that may be implemented by an embodiment;

FIG. 4 depicts a sample user interface that may be implemented by anembodiment; and

FIG. 5 depicts a system upon which TCA deployment and signaloptimization for event planning may be implemented in accordance with anembodiment.

DETAILED DESCRIPTION

Exemplary embodiments include a traffic prediction and optimization(TPO) tool for use in traffic control agent (TCA) deployment and trafficsignal optimization planning. Event planning can include plans tomitigate traffic congestion, both vehicular and pedestrian. Mitigationplans can include placement of TCAs (e.g., police officers, crossingguards) and setting traffic signals to particular settings based onexpected traffic patterns (both vehicular and pedestrian). Embodimentsof the TPO tool described herein are used to generate a two-levelplanning model for monthly and daily planning that can concurrentlyoptimize both TCA deployment and traffic signal settings. In addition,the planning model can also be used to predict traffic queue dynamicsunder different event conditions. Further, the planning model can beused to mitigate traffic congestion through generating suggestions tominimize both costs due to traffic delays and costs due to TCAdeployment.

Large scale special planned events (e.g., sporting events and parades)often attract high volumes of both pedestrians and vehicles. These highvolumes may include significant non-recurrent congestion as well asqueue spillover (e.g., traffic backed up over several blocks, gridlock).As used herein, the term “non-recurrent congestion” refers to trafficcongestion caused by the occurrence of an event with characteristics ofthe non-recurrent congestions related to the event. This is contrastedto background traffic or recurrent congestion which occurs on a regularbasis (daily, weekly). Embodiments of the TPO tool described herein canbe used by a traffic operation center or other planning entity, tooptimize the deployment of TCAs to mitigate traffic congestion caused bythe large scale events. The tool can also be used to recommend controltraffic signal settings to further mitigate the traffic congestion.

The use of traffic signal control alone is generally not adequate formanaging the congestion caused by large scale events. Automated trafficsignals are not “smart” nor are they adaptive enough to accommodate bigsurges of traffic caused by large scale events. In addition, a largevolume of pedestrians can be present at events and thus the potentialfor vehicle-pedestrians collisions. Event based signal control cannot beused to recognize, in real-time, overall traffic conditions (e.g.,including pedestrians) and can require human intervention to be changedor reset. TCAs can be deployed to increase both the safety and themobility of event traffic. The use of live TCAs results in congestionmanagement that is adaptive to different event scenarios. However, thetypical method of deploying TCAs based on historical experience, orsubject matter experts (SMEs), does not take advantage of analyticalmodeling as described herein in reference to embodiments.

The analytical modeling described herein is adaptable to differentscenarios of events and can be used to quantify and predict a cost(e.g., of TCAs, of traffic delays) associated with different TCAdeployments and traffic signal settings. Embodiments can be used toquantify the total system costs, including travel costs and TCA laborcosts. In addition, the models can be validated and results used toimprove future models. Embodiments of the TPO tool suggest locations forTCA manned control as well as settings for traffic signals.

TCAs can include both local TCAs physically posted at particularintersections as well as remote TCAs. Cameras can be located atintersections (or other points on a roadway) and a TCA can be monitoringthe cameras from a remote location. The TCA, or other entity, can thenset the traffic signals remotely based on what the TCA observes on thecameras at the remote location. In an embodiment, a computer ismonitoring the cameras from the remote location instead of or inaddition to a TCA.

Embodiments can be used to aid event traffic control operators in betteroptimizing the deployment of TCA resources to mitigate trafficcongestion caused by events. Embodiments include a multiple level TCAplanning mechanism for providing offline planning that includes monthlyand daily planning levels, as well as online planning (e.g., forunplanned events or adjustments to offline planning). The planningmechanism can be configured to concurrently take different modes oftraffic into account (i.e., multi-modal traffic) such as passengervehicles and pedestrians. Embodiments include a TPO tool that uses amodel for optimizing deployment of TCAs to minimize a cost that includesnetwork (e.g., roadway) delay costs and TCA costs. In addition, trafficlink-level (roadway segment) queue dynamics (e.g., the number ofvehicles waiting for a light at time “t”) can be predicted by optimizedTCA plans and signal plans.

Referring now to FIG. 1, a framework for special event planning that maybe implemented by an embodiment is generally shown. In an embodiment,all or a portion of the framework may be implemented by a softwareapplication executing on a computer processor. The origin-destinationestimation (ODE) block 102 can receive information about traffic volume112, existing road closures and detours 114, link capacity 116, andbackground origin-destination (OD) path 118 (e.g., paths taken bytraffic that travels the OD path on a typical day, does not take intoaccount additional traffic caused by an event) for roadways within aroadway network of interest. In addition, SME knowledge from day-to-dayevent operations 110 can be input to the ODE block 102. The ODE block104 can generate background link travel time data 120 (e.g., travel timeon a segment of street) and background OD demand data 122 (e.g., numberof trips from an origin to a destination). The ODE block 102 can be usedto obtain a background OD trip matrix which describes how many tripswill be generated from origins to destinations at a particular time(s)without taking into account additional demand caused by an event.

The responsive rerouting (RR) block 104 of FIG. 1 can receiveinformation about event-based road closures 124, event-based turnrestrictions 126 and event-based detours 128, as well as SME knowledgefrom day-to-day event operations 110 and outputs from the ODE block 102.The RR block 104 can generate an adjusted background OD path 130. Thus,given event-based control plans, such variable message sign (VMS)locations, road closures, turning restrictions and detours, the RR block104 can find possible alternative routes for typical time-of-day traffic(e.g., background traffic that does not take events into account).

The event-based traffic assignment (ETA) block 106 of FIG. 1 can receiveinformation about event OD estimation 132 (e.g., an event OD trip matrixwhich describes how many trips will be generated from origins todestinations at a particular time(s) due to the event), event OD demand134 (e.g., number of trips from an event origin to an eventdestination), event OD path 136, and link capacity 138, as well as SMEknowledge from day-to-day event operations 110 and outputs from the RR104 block. The ETA block 106 can generate all path flow data 140 andturning ratios data 142. Thus, keeping the non-event path flowunchanged, the ETA block 106 can re-assign event traffic and affectedtime-of-day traffic which are estimated by event control plans. The ETAblock 106 can then calculate and output turning ratio data 142 whichdescribes expected paths at each intersection.

The traffic prediction and optimization (TPO) block 108 of FIG. 1 canreceive information about road closures 124, time-of-day signal plans144 (e.g., signal cycle settings) and TCA resources 146 (e.g., thenumber of TCAs available), as well as SME knowledge from day-to-dayevent operations 110 and outputs from the previous blocks. The TPO block108 can generate link flow data 148 (e.g., traffic flow rate, number ofvehicles per hour) and link density data 150 (e.g., number of vehiclesper mile). Thus, the TPO block 108 can simulate traffic dynamics (alsoreferred to herein as “traffic flow”) given by the demand defined by amodel generated by the previous blocks (e.g., the ODE block 102, the RRblock 104, and the ETA block 106). In addition, the TPO block 108 canalso perform signal plan optimization and TCA planning based on thesimulated traffic dynamics.

FIG. 2 depicts a more detailed view of an embodiment of inputs andoutputs of processes that can be performed by the TPO block 108 shown inFIG. 1. The TPO block 108 can be implemented by an embodiment of a TPOtool executing on a computer processor. In an embodiment, the TPO block108 can simulate traffic dynamics based on a given demand. It can alsoperform TCA planning, including signal optimization for roadways withina transportation network of interest. The scope of the transportationnetwork of interest may be modified, based on, for example, a locationof an event. The TCA planning shown in FIG. 2 includes offline TCAplanning 202 and online TCA planning 218. Offline TCA planning 202 caninclude monthly TCA planning 204 and daily TCA planning and signaloptimization 206. Inputs to monthly TCA planning 204 can include anevent schedule 222 (e.g., expected times for upcoming events), TCAresources 146, and TCA shift constraints 224 (e.g., maximum number ofconsecutive shifts, maximum number of shifts in a time period, etc.).The monthly TCA planning 204 includes logic to create a monthly plan forTCA placement. An output from the monthly TCA planning 204 can includean optimal TCA monthly plan 220. An embodiment of the TCA monthly plan220 includes TCA event assignment, TCA location assignment, and TCAshift assignment.

Inputs to daily TCA planning and signal optimization 206 can includeoutputs from the monthly TCA planning 204 (e.g., the optimal TCA monthlyand daily plan 220), TCA resources 146, time-of-day signal plans 144,road closures 124 (e.g., expected road closures for particulardays/times), traffic/event demand 140 (e.g., taking into account trafficcaused by the event), and turning ratios 142 (e.g., expected by theevent traffic). The daily TCA planning and signal optimization 206includes logic to simulate traffic dynamics. Assumptions and/oroptimization parameters used by the TCA planning and signal optimization206 can be varied to allow for different scenarios to be simulated.Examples of assumptions that may be varied include event traffic volumeis known or can be estimated, and TCA's performance is identical to eachother. Examples of optimization parameters that may be varied includedifferent geometry of intersections, different signal planconfigurations, and different event types.

Outputs from the daily TCA planning and signal optimization 206 caninclude traffic predictions 214 and optimized plans 208. The trafficprediction 214 simulates traffic dynamics (e.g., based on demand, TCAplacement, traffic signal settings) and can include network congestionpredictions 216 (e.g., predicting network flow rates and networkdensity), link flow predictions 148 (e.g., predicting number of vehiclesper hour in a link), and link density predictions 150 (e.g., predictingnumber of vehicles per mile in a link). Based on “what-if” analyticsthat can include changing a placement of one or more TCAs and trafficsignal settings, different network congestion predictions 216, link flowpredictions 148 and link density predictions may result. These can beassigned a cost and a model used to minimize the cost associated withthe combination of TCA deployment and traffic delay (both pedestrian andvehicular). The output of the model, the optimized plans 208 can includean optimal TCA daily plan 210 (e.g., a schedule of TCA placementthroughout the day) and an optimal signal plan 212 (e.g., a schedule ofcycle times throughout the day for signals within the network).

Online TCA planning 218 can be used to adjust the settings of trafficsignals (e.g., by a TCA) and/or the deployment of TCAs based on actualtraffic patterns observed. These adjustments can be input to the modelto improve future model predictions. Adjustments can also be made due toother factors such as fewer TCAs being available than planned for and/oran unplanned event that impacts traffic patterns or TCA availability.

FIG. 3 depicts an embodiment of a process flow for performing daily TCAplanning and signal optimization in accordance with an embodiment. Atblock 302, a background OD trip matrix and event OD trip matrix areobtained which describe an estimate of how many trips are to begenerated from origins to destinations in the transportation network ata particular time(s). In an embodiment, a transportation network refersto a group of transportation network units, such as, but not limited toroads and sidewalks within a specified geographic location or within adistance of a specified geographic location. The network units can bemade up of intersections and links (pathways between the intersections).At block 304, event-based control plans, such as, but not limited to,VMS locations, road closures 124, turning restrictions 126, and detours128 can be obtained. At block 306, event traffic and affected time ofday traffic can be re-assigned based on the event-based control plansobtained in block 304. At block 308, turning ratios can be calculated atthe intersections within the transportation network. The turning ratiocan includes an estimate of paths that will be taken by vehicles orpedestrians that are coming from an origin on the way to a destination.An example is an estimate of how many east-bound will turn left at aparticular intersection.

At block 310, traffic queue dynamics can be analyzed by a traffic modelto generate, for example, an estimate of the flow capacity (e.g., numberof vehicles per hour on each link at a particular time) and a queue ateach link at the particular time. At block 312, signal plan optimizationand TCA planning are performed to determine placement of TCAs andsettings for traffic signals. The optimization can be performed tominimize a cost associated with traffic delays and TCA deployment. Theoptimization can be within the constraints of a specified number of TCAsand a set number of traffic signals in the transportation network.

Turning now to FIG. 4, a sample user interface that may be implementedby an embodiment is generally shown. The user interface shown in FIG. 4can be a screen interface that is generated by an embodiment of the TPOtool and displayed on a user device. The screen includes a planning timerange section 402, an event section 404, a TCA planning section 406, amap section 408, and a calendar section 410 for a traffic resourcecontrol plan. In an embodiment, the calendar section 410 can allow auser to scroll to view traffic resource control plans for differentcalendar dates and times. In an embodiment, the map section 408 candisplay all or a portion of a transportation network that is included inthe traffic control resource plan. The map section 408 shows thesuggested placement of the TCAs. The TCA planning section 406 caninclude TCA data related to the plan output from the model. Though notshown in FIG. 4, a traffic control signal plan user interface screen caninclude traffic control signal related data. The event section 404 caninclude information about planned events. The planning time rangesection 402 can include information about the time frame that the planrelates to.

An embodiment of a model for performing signal plan optimization and TCAplanning follows. Goals of embodiments of the model include minimizing atotal cost which includes costs associated traffic and TCA deploymentcosts. Assumptions used by embodiments of the model include: demand isknown; turning ratio is fixed and traffic rerouting is not allowed; andcycle length of traffic signals is fixed. In embodiments, the saturationflow of each link and the turning rates are assumed to be known andconstant.

Sets Input and Used by an Embodiment of the Model Follow:

nεN, the set of all nodes, where the road network is represented as adirected graph with links “i” and intersections “n”;

nεN_(c)⊂N, the set of nodes needed for signal optimization;

iεI, the set of all vehicle links;

mεM, the set of all pedestrian links;

tεT, the set of all time steps;

(i, n, j)εK≡I×N×I, the set of all turns, from link i, via node n, tolink

(i, n, j)εK_(c)≡I×N_(c)×I, the set of turns, located in the nodes neededfor signal optimization;

zεZ, the set of vehicle signal stages, the signal control plan of anintersection n (or node n) may be based on a fixed number of vehiclestages;

zεZ′_(inj), the set of vehicle signal stages at node n, serving turn (i,n, j); and

pεP, the set of pedestrian signal phases.

Data Inputs to an Embodiment of the Model Follow:

d_(it)∀iεI, tεT, traffic demand (vehicles/hour) on link i, at time stept;

a_(ij)∀iεI, jεI, turning ratios from link j to link i, a_(ij) is zero ifthere is no connection between links i and j.

Q_(it)∀iεI, tεT, flow capacity (vehicles/hour) on link i, at time step t(obtained for example by link width, number of lanes and shoulderwidth);

x_(0i) ∀iεI, initial queue (vehicle) on link i;

x_(max i) ∀iεI, storage capacity (vehicle) on link i;

P_(mt) ∀mεM, tεT, pedestrian capacity (pedestrians/hour) on link m, attime step t;

λ_(mt) ∀mεM, tεT, pedestrian arrival rate (pedestrians/hour) on link m,at time step t;

y_(0m) ∀mεM; initial pedestrian queue (pedestrian) on link m;

Y_(max,m) •MεM, pedestrian storage capacity (pedestrian) on link m;

Δ, time step interval (e.g., hour, minute);

Ω_(inj) ∀(i, n, j)εK\ K_(c), turning capacity (vehicles/hour) at turn(i, n, j);

S_(inj) ∀(i, n, j)εK_(c), saturation flow rate (vehicles/hour) on turn(i, n, j), where saturation flow rate is the maximal allowable flow ratein a link;

R_(m) ∀mεM, saturation flow rate (pedestrians/hour) on pedestrian linkm;

G_(nz) ∀nεN_(c), zεZ, background green time at node n and vehicle stages, where background green time is offline pre-optimized green time and avehicle stage s is a certain period of time in which the signal of oneor several traffic movement is green (a stage could contain multiplecompatible phases);

G_(np) ∀nεN_(c), pεP, background green time at node n and pedestrianphase p, where pedestrian phase p is a certain period of time in whichthe signal of one pedestrian movement is green;

Gwalk_(np) ∀nεN, pεP, the background green time for pedestrians to walkat node n and pedestrian phase p;

D_(np) ∀nεN_(c), pεP, don't walk green time for pedestrians at node nand pedestrian phase p;

g_(nzt) ^(min) ∀nεN, zεZ, tεT, the minimal green time (seconds) at noden and vehicle stage s;

g_(nzt) ^(max) ∀nεN, zεZ, tεT, the maximal green time (seconds) at noden and vehicle stage s;

C_(n) ∀nεN, cycle length (seconds) at node n;

T2S_(turn), vehicle green stages matrix which represents a mapping fromvehicle links to stages;

P2S_(turn), pedestrian green phase matrix which represents a mappingfrom pedestrian links to stages;

L, lost time in a cycle which represents unused green time which novehicles are able to pass through an intersection despite the trafficsignal displaying a green (go) signal;

A_(zp) ∀zεZ, pεP, relationship between vehicle stage z and pedestrianphase p, for example a given intersection may have west/east (WE)traffic signals and north/south (NS) traffic signals, the vehicletraffic signal can have four stages (turn WE, WE, turn NS, NS) and thepedestrian traffic signal can have two phases (walk WE, walk NS), thematrix A_(zp) can be generated to reflect the relationships, forexample, A₁₁=0 can reflect that when the vehicle stage is turn WE,pedestrian phase walk WE is not green, and A₂₁=1 can reflect that whenthe vehicle stage is WE, pedestrian phase walk WE is green;

B_(n) ∀nεN_(c)⊂N, the number of TCAs needed at node n;

τ^(min), the minimal number of time steps that a TCA needs to be workingat an intersection;

C_(init), initial cost for a TCA which can includes overhead costs suchas vehicles, health insurance;

C_(hr), hourly cost for a working TCA; and

W, is an integer that is used for constraint selections.

Decision Variables Used by an Embodiment of the Model Follow:

x_(it) ∀iεI, tεT, queue on link i (e.g., the number of vehicles at roadsegment x), at time step t;

u_(it) ∀iεI, tεT, outflow on link i, at time step t, this represents thenumber of vehicles that leave the link;

y_(mt) ∀mεM, tεT, queue on link m (e.g., the number of pedestrians atsidewalk segment y), at time step t;

s_(mt) ∀mεM, tεT, pedestrian outflow on link m, at time step t, thisrepresents the number of pedestrians that leave the link;

g_(nzt) ∀nεN_(c), zεZ, tεT, optimized green time at node n, vehiclestage z and time step t, where optimized green time refers to best greentime obtained which serves the objective best;

g_(npt) ∀nεN_(c), pεP, tεT, optimized green time at node n, pedestrianphase p and time step t;

gwalk_(npt) ∀nεN_(c), pεP, tεT, the green-walking time for pedestriansat node n, phase p and time step t;

ω_(nt) ∀nεN, tεT, a binary decision variable, ω_(nt)=1 when a TCA shouldbe assigned to work at intersection n at time step t, otherwiseω_(nt)=0,

B_(max) is a decision variable that represents the maximum (or total)number of TCAs that are needed in the network.

Objective:

The objective of an embodiment of the model is to minimize total delayand TCA deployment costs. The objective function can be expressed as:

${{Minimize}\mspace{14mu}\alpha{\sum\limits_{t}{\sum\limits_{i}x_{it}}}} + {\beta{\sum\limits_{t}{\sum\limits_{m}y_{mt}}}} + {C_{init}B_{\max}} + {\sum\limits_{n}{\sum\limits_{t}{C_{hr}B_{n}\omega_{nt}}}}$

$\sum\limits_{t}{\sum\limits_{i}x_{it}}$

represents the total vehicle queue at link i, at time t.

$\sum\limits_{t}{\sum\limits_{m}y_{mt}}$

represents the total pedestrian queue at pedestrian link m, at time t.

C_(init)B_(max) represents the total initial cost for TCAs.

$\sum\limits_{n}{\sum\limits_{t}{C_{hr}B_{n}\omega_{nt}}}$

represents the total duration cost for TCAs.

α and β are the coefficients to convert time (e.g., traffic delay) intoUnited States (U.S.) dollars. α can be applied to determine the cost ofa vehicle waiting and β cam be applied to determine the cost of apedestrian waiting. To translate travel delays into monetary values, theU.S. Department of Transportation (DOT) has used the following traveltime values for evaluating transportation projects (1997 U.S. dollars):in-vehicle time at $8.90/person-hour; out-of-vehicle time (e.g., waitingfor a bus at $17.00/person-hour; and commercial truck at$16.50/person-hour. An embodiment uses these same values adjusted for aconsumer price index (CPI) inflation of 143.54% from 1997 to 2012.

In an embodiment, the objective function is subject to severalconstraints including: flow conservation constraints (vehicle andpedestrian), turning flow constraints (vehicle and pedestrian), signalplan constraints, and TCA planning constraints. Exemplary embodiments ofthese constraints follow.

Vehicle Flow Conservation Constraints.

These constraints reflect vehicle queue dynamics from one vehicle linkto another. For example, the dynamics of a given vehicle link i may beexpressed as the conservation equation below:

${x_{i,{t + 1}} = {x_{it} + {\left( {{\sum\limits_{j}{a_{ij}u_{jt}}} + d_{it} - u_{it}} \right)\Delta\mspace{14mu}{\forall{i \in I}}}}},{t \in T}$x_(i 1) = x_(0i)  ∀i ∈ I 0 ≤ x_(it) <  = x_(max  i)  ∀i ∈ I, t ∈ T

Pedestrian Flow Conservation Constraints.

These constraints reflect pedestrian queue dynamics from one pedestrianlink to another. For example, the dynamics of a given pedestrian link mmay be expressed as the conservation equation below:y _(m,t+1) =y _(mt)+(λ_(mt) −s _(mt))Δ∀mεM,tεTy _(m1) =y _(0m) ∀mεM0≦y _(mt) ≦=y _(max,m) ∀mεM,tεT

Vehicle Turning Flow Constraints.

Vehicle flow capacity is represented as:0≦u _(it) ≦Q _(it) ∀iεI,tεT

Vehicle turning capacity is represented as:a _(ji) u _(it)≦Ω_(inj)∀(i,n,j)εK\K _(c) ,tεT

In an embodiment, the vehicle saturation flow rate constraint isrepresented as two mutual exclusive constraints, when ω_(nt) is equal to1, the first constraint is active to ensure vehicle flow rate is lessthan the maximal allowable flow limited by assigned green times. Whenω_(nt) is equal to zero, the second constraint is active to ensurevehicle flow rate is less than the maximal allowable flow limited byoffline pre-defined green times.

${{a_{ji}u_{it}} \leq {{S_{inj}\frac{\sum\limits_{z}{g_{nzt}*T\; 2S_{turn}}}{C_{n}}} + {\left( {1 - \omega_{nt}} \right)*W\mspace{14mu}{\forall{\left( {i,n,j} \right) \in K_{c}}}}}},{t \in T}$${{a_{ji}u_{it}} \leq {{S_{inj}\frac{\sum\limits_{z}{G_{nz}*T\; 2S_{turn}}}{C_{n}}} + {\omega_{nt}*W\mspace{14mu}{\forall{\left( {i,n,j} \right) \in K_{c}}}}}},{t \in T}$

Pedestrian Flow Constraints.

Pedestrian flow capacity is represented as:0≦s _(mt) ≦P _(mt) ∀mεM,tεT

Pedestrian saturation flow rate constraint is represented as two mutualexclusive constraints. When ω_(nt) is equal to 1, the first constraintis active to ensure pedestrian flow rate is less than the maximalallowable flow limited by assigned green times. When ω_(nt) is equal tozero, the second constraint is active to ensure pedestrian flow rate isless than the maximal allowable flow limited by offline pre-definedgreen times.

${s_{mt} \leq {{R_{m}\frac{\sum\limits_{p}{{gwalk}_{np}*P\; 2S_{turn}}}{C_{n}}} + {\left( {1 - \omega_{nt}} \right)*W\mspace{14mu}{\forall{\left( {i,n,j} \right) \in K_{c}}}}}},{t \in T}$$\mspace{79mu}{{s_{mt} \leq {{R_{m}\frac{\sum\limits_{p}{{Gwalk}_{npt}*P\; 2S_{turn}}}{C_{n}}} + {\omega_{nt}*W\mspace{14mu}{\forall{\left( {i,n,j} \right) \in K_{c}}}}}},{t \in T}}$

Signal Plan Constraints.

An embodiment of modeling traffic signals for an optimized signalincludes, by definition, that the constraint:

${{{\sum\limits_{z}g_{nzt}} + L} = {C_{n}\mspace{14mu}{\forall{n \in N_{c}}}}},{t \in T}$holds at each intersection n, where g_(nzt) is the optimized green timeof stage z at node n at time step t; and C_(n) is the cycle length atnode n.

In addition:g _(nz) ^(min) ≦g _(nzt) ≦g _(nz) ^(max) ∀nεN,zεZ,tεTwhere g^(min) _(nz) and g^(max) _(nz) are the minimum and maximumpermissible green time for stage z at node n, respectively.

Further:G _(np) =G _(nz) ·A _(zp) ∀nεN _(c) ,zεZ,pεPGwalk_(np) =G _(np) −D _(np) ∀nεN _(c) ,pεPg _(npt) =g _(nzt) ·A _(zp) ∀nεN _(c) ,zεZ,pεP,tεTgwalk_(npt) =g _(npt) −D _(np) ∀nεN _(c) ,pεP,tεT

TCA Planning Constraints.

TCA planning constraints can be relaxed in the first stage, which youcan understand the best number of TCA needed and how should you assignthem in each location and time slots without considering logistics orother constraints of TCAs.

Satisfy Minimum Working Time:

${{\sum\limits_{k = t}^{t + \tau_{\min}}{{\omega_{nk} - \omega_{n,{k - 1}}}}} \leq {1\mspace{14mu}{\forall t}}},{n \in N}$

Maximal TCA Needed at Each Time Step:

${\sum\limits_{n}{B_{n}\omega_{nt}}} \leq {B_{\max}\mspace{14mu}{\forall{t \in T}}}$

Turning now to FIG. 5, a system 500 upon which a TPO tool may beimplemented in an embodiment will now be described.

The system 500 includes a host system computer 502 and communicationdevices 504 communicatively coupled to one or more network(s) 506. Thehost system computer 502 may be implemented as one or more high-speedcomputer processing devices, such as one or more mainframe computers orservers capable of handling a high volume of computing activitiesconducted by end users of the social interaction facilitation tool. Thehost system computer 502 may operate as a database server and coordinateaccess to application data including data stored on a storage device510. The storage device 510 may be implemented using memory contained inthe host system computer 502 or may be a separate physical device. In anembodiment, the storage device 510 stores data associated with themulti-level framework for TCA planning, such as data associated with theoptimization 208 and traffic prediction 214 shown in FIG. 2.

The host system computer 502 may be implemented using one or moreservers operating in response to a computer program stored in a storagemedium accessible by the server. The host system computer 502 may alsooperate as a network server (e.g., a web server) to communicate with thecommunications devices 504, as well as any other network entities. In anembodiment, the host system computer 502 may represent a node in a cloudcomputing environment or may be configured to operate in a client/serverarchitecture.

The communications devices 504 may be any type of devices with computerprocessing capabilities. For example, the communications devices 504 mayinclude a combination of general-purpose computers (e.g., desktop, laptop), host-attached terminals (e.g., thin clients), and portablecommunication devices (e.g., smart phones, personal digital assistants,and tablet PCs). The communications devices 504 may be wired or wirelessdevices. In an embodiment, the communications devices 504 may representcloud consumers in a cloud computing environment.

In an embodiment, the communications devices 504 may be implemented byend users of a website or web service hosted by an entity or enterpriseoperating the host system computer 502. The communications devices 504may each execute a web browser for accessing network entities, such asthe host system computer 502. In an embodiment, the communicationsdevices 504 access a web site of the host system computer 502 forbrowsing and accessing an application 512. The application 512implements the TPO tool and any other processes described herein.

The network(s) 506 may be any type of known networks including, but notlimited to, a wide area network (WAN), a local area network (LAN), aglobal network (e.g. Internet), a virtual private network (VPN), and anintranet. The network(s) 506 may be implemented using a wireless networkor any kind of physical network implementation known in the art, e.g.,using cellular, satellite, and/or terrestrial network technologies.

The system 500 also includes storage devices 508 communicatively coupledto the host system computer 502. The storage devices 508 may belogically addressable as consolidated data sources across a distributedenvironment that includes a network (e.g., network(s) 506). In anembodiment, the storage devices 508, as well as the storage device 510,represent the data sources used by embodiments of the TPO tool. Thestorage devices 508 can store, along with or in place of storage device510, data associated with the multi-level framework for TCA planning.

Technical effects and benefits include providing assistance to eventtraffic operators to optimize deployment of traffic control agencyresources to mitigate traffic congestion caused by events. Embodimentscan include a multiple level planning mechanism, including monthly,daily and online planning. Embodiments can also include the ability toconcurrently take multi-modal traffic into account, including bothpassenger vehicles and pedestrians. In addition, an analytical model canbe provided to optimize deployment of traffic control agency to minimizetotal network delay. Still further traffic link-level queue dynamics canbe predicted for optimized TCA deployment plans and traffic signalplans.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiments were chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

Further, as will be appreciated by one skilled in the art, aspects ofthe present disclosure may be embodied as a system, method, or computerprogram product. Accordingly, aspects of the present disclosure may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method of traffic control resource planning,the method comprising: receiving information about available routes in atransportation network; receiving an estimate of a traffic demand in thetransportation network; performing traffic control planning, theperforming including: simulating a traffic flow based on the availableroutes and the traffic demand; applying a model that varies trafficcontrol agent (TCA) placement and traffic signal settings in thetransportation network to minimize a cost associated with the trafficflow, the cost including a TCA deployment cost and a traffic delay cost;and outputting a traffic control plan based on the applying, the trafficcontrol plan including a TCA placement and traffic signal setting plan;monitoring, via a camera, an actual traffic flow on one or more of theavailable routes; and updating the traffic control plan based on themonitoring.
 2. The method of claim 1, wherein the traffic demand isdetermined based on a planned event.
 3. The method of claim 1, whereinthe traffic demand is determined based on an unplanned event.
 4. Themethod of claim 1, wherein the traffic flow includes vehicular trafficand pedestrian traffic.
 5. The method of claim 1, wherein thetransportation network includes vehicular roadways and pedestrianroadways.
 6. The method of claim 1, wherein a number of TCAs availablefor TCA placement is fixed.
 7. The method of claim 1, wherein the TCAplacement takes into account TCA shift constraints.
 8. The method ofclaim 1, wherein the model is further applied to quantitatively predicta difference in the cost between the traffic control plan and a secondtraffic control plan.
 9. A computer program product for traffic controlresource planning, the computer program product comprising: anon-transitory computer readable storage medium having program codeembodied therewith, the program code executable by a computer toimplement: receiving information about available routes in atransportation network; receiving an estimate of a traffic demand in thetransportation network; and performing traffic control planning, theperforming including: simulating a traffic flow based on the availableroutes and the traffic demand; applying a model that varies trafficcontrol agent (TCA) placement and traffic signal settings in thetransportation network to minimize a cost associated with the trafficflow, the cost including a TCA deployment cost and a traffic delay cost;and outputting a traffic control plan based on the applying, the trafficcontrol plan including a TCA placement and traffic signal setting plan;monitoring, via a camera, an actual traffic flow on one or more of theavailable routes; and updating the traffic control plan based on themonitoring.